Spring I/O 2025 - 21-23 May, Barcelona
Slides: https://docs.google.com/presentation/...
As AI applications become increasingly integrated into critical applications, ensuring their reliability, safety, and fairness is more challenging than ever. Unlike traditional software, AI models are dynamic and data-driven and often behave unpredictably in real-world scenarios. This makes testing AI systems a complex endeavor that requires specialized approaches. In this session, we will explore the unique challenges of testing AI applications, such as handling probabilistic outputs, bias detection, model drift, and transparency in decision-making.
We’ll also cover how to use AI to develop better tests or generate synthetic data to make tests more real than random data. Whether you’re involved in AI development or responsible for system validation, this session will equip you with the essential skills to confidently tackle AI testing challenges and ensure robust, reliable AI solutions.
